Refine
Document Type
- Article (reviewed) (3) (remove)
Language
- English (3) (remove)
Is part of the Bibliography
- yes (3)
Keywords
- Computer Games (1)
- Computer Science (1)
- Computerspiele (1)
- Game Design (1)
- Games (1)
- Gamification (1)
- Human Computer Interaction (1)
- Human Computrer Interaction (1)
- Human-Robot Interaction (1)
- Loneliness (1)
Institute
- Fakultät Medien und Informationswesen (M+I) (bis 21.04.2021) (3) (remove)
Open Access
- Open Access (2)
- Closed Access (1)
The findings presented in this article were obtained through a preliminary exploratory study conducted at the Offenburg University as part of the Fighting Loneliness project promoted by the institution’s Affective & Cognitive Institute (ACI) from October 2019 to February 2020. The initiative’s main objective was to answer the research question “How should an app be designed to reduce loneliness and social isolation among university students?” with the collaboration of the institution’s students.
In this article, we present a taxonomy in Robot-Assisted Training; a growing body of research in Human–Robot Interaction which focuses on how robotic agents and devices can be used to enhance user’s performance during a cognitive or physical training task. Robot-Assisted Training systems have been successfully deployed to enhance the effects of a training session in various contexts, i.e., rehabilitation systems, educational environments, vocational settings, etc. The proposed taxonomy suggests a set of categories and parameters that can be used to characterize such systems, considering the current research trends and needs for the design, development and evaluation of Robot-Assisted Training systems. To this end, we review recent works and applications in Robot-Assisted Training systems, as well as related taxonomies in Human–Robot Interaction. The goal is to identify and discuss open challenges, highlighting the different aspects of a Robot-Assisted Training system, considering both robot perception and behavior control.
This work demonstrates the potentials of procedural content generation (PCG) for games, focusing on the generation of specific graphic props (reefs) in an explorer game. We briefly portray the state-of-the-art of PCG and compare various methods to create random patterns at runtime. Taking a step towards the game industry, we describe an actual game production and provide a detailed pseudocode implementation showing how Perlin or Simplex noise can be used efficiently. In a comparative study, we investigate two alternative implementations of a decisive game prop: once created traditionally by artists and once generated by procedural algorithms. 41 test subjects played both implementations. The analysis shows that PCG can create a user experience that is significantly more realistic and at the same time perceived as more aesthetically pleasing. In addition, the ever-changing nature of the procedurally generated environments is preferred with high significance, especially by players aged 45 and above.